Previous approaches resolve the mask conflict between neighboring objects through non-maximum suppression or additional post-processing [1]}, [2]}, [3]}, [4]}, [5]}. Consequently, their results are over-smooth along boundaries or exhibit small gaps between neighboring objects. Furthermore, since the receptive field in the ROI observes multiple objects that belong to the same class, when the occluding regions were included as part of the occluded object, traditional mask head design falls short of resolving such conflict, leaving a large portion of error as shown in Figure REF .
We compare BCNet with recent amodal segmentation methods [6]}, [7]}, which predict complete object masks, including the occluded region.
However, these amodal methods only regress single occluded target in the ROI, thus lacking occluder-occludee interaction reasoning, making their specially designed decoupling structure suffer when handling mask conflict between highly-overlapping objects.
Correspondingly,
Figure REF  compares the architecture of our BCNet with previous mask head designs [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [6]}, [7]}.
